## Nombre de participants se déclarant comme joueurs :  29
## Nombre de femmes se déclarant comme joueuses :  3
## Age médian des joueurs :  15

Removing Outliers based on BET

## [1] "Outliers BET STANDARD DEVIATION: 3qq8dp8jk, 79pn8m6v8, e58u3sinl, urgv6o806"

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers BET SAVED SHEEPS: "
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers BET EXPLOIT DDA: vuq3c2tk6"
## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Total number of outliers:  5"
## [1] "Total number of outliers motor task:  2"
## [1] "Total number of outliers perceptive task:  1"
## [1] "Total number of outliers logical task:  2"

Removing Outliers based on CONFIDENCE SCALE

## [1] "Outliers CS STANDARD DEVIATION: 9b3ph38yc, a6dfu5ljd, dyg7cga2o, tmxmxmwhi, zp9bc59o5"
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Total number of outliers:  5"
## [1] "Total number of outliers motor task:  0"
## [1] "Total number of outliers perceptive task:  5"
## [1] "Total number of outliers logical task:  0"

Modeling difficulties

Modeling objective difficulty for motor task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1953.7   1975.3   -972.8   1945.7     1620 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.1396 -0.7500  0.2888  0.7385  2.8481 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.5631   0.7504  
## Number of obs: 1624, groups:  IDjoueur, 56
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.0298     0.1873  -5.499 3.83e-08 ***
## difficulty    2.9618     0.2146  13.803  < 2e-16 ***
## timeNorm     -0.5280     0.2020  -2.614  0.00895 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.539       
## timeNorm   -0.571 -0.009
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0      1624         0 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-1.050110  
##  1st Qu.:-0.438217  
##  Median :-0.118832  
##  Mean   :-0.002364  
##  3rd Qu.: 0.296005  
##  Max.   : 1.658440  
## [1] "Intercept: -1.03 3.8e-08 ***"
## [1] "Difficulty: 2.96 2.4e-43 ***"
## [1] "Time: -0.528 0.009 **"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.29"
## [1] "Cross Val: 0.69"
## [1] "AIC: 2000"
##         0%        25%        50%        75%       100% 
## -1.6584395 -0.2960052  0.1188317  0.4382172  1.0501105

##         0%        25%        50%        75%       100% 
## -1.6584395 -0.2960052  0.1188317  0.4382172  1.0501105

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for sensory task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1261.1   1282.7   -626.5   1253.1     1620 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.3943 -0.3586  0.1131  0.3536  6.6338 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.7241   0.8509  
## Number of obs: 1624, groups:  IDjoueur, 56
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -3.3288     0.2583 -12.885   <2e-16 ***
## difficulty    8.2778     0.4068  20.346   <2e-16 ***
## timeNorm     -0.2933     0.2674  -1.097    0.273    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.650       
## timeNorm   -0.519 -0.046
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 2.21089 (tol =
## 0.001, component 1)
## The result is correct only if all data used by the model has not changed since model was fitted.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 2.21089 (tol =
## 0.001, component 1)
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0         0      1624 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-1.6765404  
##  1st Qu.:-0.4435738  
##  Median : 0.0778425  
##  Mean   :-0.0007671  
##  3rd Qu.: 0.4353921  
##  Max.   : 1.5192471  
## [1] "Intercept: -3.33 5.5e-38 ***"
## [1] "Difficulty: 8.28 5e-92 ***"
## [1] "Time: -0.293 0.27 :("
## [1] "R2 fixed: 0.34"
## [1] "R2 mixed: 0.44"
## [1] "Cross Val: 0.82"
## [1] "AIC: 1300"
##          0%         25%         50%         75%        100% 
## -1.51924712 -0.43539206 -0.07784249  0.44357377  1.67654045

##          0%         25%         50%         75%        100% 
## -1.51924712 -0.43539206 -0.07784249  0.44357377  1.67654045

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for logical task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1426.5   1447.8   -709.2   1418.5     1504 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.9435 -0.5021 -0.1156  0.5089  4.9862 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 1.577    1.256   
## Number of obs: 1508, groups:  IDjoueur, 52
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.8650     0.2652  -7.033 2.01e-12 ***
## difficulty    5.6686     0.3206  17.680  < 2e-16 ***
## timeNorm     -1.9313     0.2573  -7.507 6.04e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.496       
## timeNorm   -0.378 -0.227
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##      1508         0         0 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-1.7902825  
##  1st Qu.:-0.7784485  
##  Median :-0.3355504  
##  Mean   :-0.0003123  
##  3rd Qu.: 0.7369882  
##  Max.   : 3.1275697  
## [1] "Intercept: -1.86 2e-12 ***"
## [1] "Difficulty: 5.67 6e-70 ***"
## [1] "Time: -1.93 6e-14 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.58"
## [1] "Cross Val: 0.8"
## [1] "AIC: 1400"
##         0%        25%        50%        75%       100% 
## -3.1275697 -0.7369882  0.3355504  0.7784485  1.7902825

##         0%        25%        50%        75%       100% 
## -3.1275697 -0.7369882  0.3355504  0.7784485  1.7902825

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Influence of Player Profiles

Player profiles

Influence of Player Profiles

Objective level and player profile

Playing video games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3815, p-value = 0.1671
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1442117

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.68759, p-value = 0.4917
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.07199342

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.30458, p-value = 0.7607
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03301126

Playing board games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.86453, p-value = 0.3873
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.08913015

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.48979, p-value = 0.6243
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.05061255

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.79975, p-value = 0.4239
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.08596507

Self efficacy and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 28 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.17852, p-value = 0.8583
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02429648
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.4833, p-value = 0.01302
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.3393258 
## 
## [1] "self.eff.on.level.s 0.34 0.013 *"
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 26 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.51036, p-value = 0.6098
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.07281435

Risk aversion and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.5679, p-value = 0.1169
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1554335

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.1214, p-value = 0.03389
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2101231 
## 
## [1] "risk.av.on.level.s 0.21 0.034 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3062, p-value = 0.1915
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1347244

Age and level for each task

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.97478, p-value = 0.3297
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.09369113
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.2162, p-value = 0.02668
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2137687 
## 
## [1] "age.on.level.s 0.21 0.027 *"
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2774, p-value = 0.2015
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1275074

Sex and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.1404, p-value = 0.03233
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2377395 
## 
## [1] "sexe.on.level.m -0.24 0.032 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.077873, p-value = 0.9379
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##          tau 
## -0.008649769

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.26928, p-value = 0.7877
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03108211

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 220, p-value = 0.03213
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.82775747 -0.05457213
## sample estimates:
## difference in location 
##             -0.4558716 
## 
## [1] "sexe.on.level.m.2 -0.46 0.032 * mean(A): 0.15 mean(B): -0.31"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 347, p-value = 0.9453
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.4361429  0.4780691
## sample estimates:
## difference in location 
##            -0.01100307

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 292, p-value = 0.7971
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.8271570  0.5994594
## sample estimates:
## difference in location 
##            -0.04046848

CONFIDENCE SCALE APPROACH

For Bet approach, see the other file.

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.130 49 7.6e-05 ***
##  2:      0.09375          0.160 57 1.3e-05 ***
##  3:      0.15625          0.110 57 0.00037 ***
##  4:      0.21875          0.140 57 2.8e-06 ***
##  5:      0.28125          0.110 58 0.00028 ***
##  6:      0.34375          0.110 57 1.5e-06 ***
##  7:      0.40625          0.069 56     0.013 *
##  8:      0.46875          0.015 58     0.42 :(
##  9:      0.53125         -0.031 56     0.13 :(
## 10:      0.59375         -0.044 58     0.056 .
## 11:      0.65625         -0.110 58 5.4e-05 ***
## 12:      0.71875         -0.130 58 2.4e-06 ***
## 13:      0.78125         -0.190 57 1.8e-08 ***
## 14:      0.84375         -0.230 55 3.7e-09 ***
## 15:      0.90625         -0.240 57 1.2e-10 ***
## 16:      0.96875         -0.190 57 9.9e-10 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 49 7.6e-05 ***
##  2: 57 1.3e-05 ***
##  3: 57 0.00037 ***
##  4: 57 2.8e-06 ***
##  5: 58 0.00028 ***
##  6: 57 1.5e-06 ***
##  7: 56     0.013 *
##  8: 58     0.42 :(
##  9: 56     0.13 :(
## 10: 58     0.056 .
## 11: 58 5.4e-05 ***
## 12: 58 2.4e-06 ***
## 13: 57 1.8e-08 ***
## 14: 55 3.7e-09 ***
## 15: 57 1.2e-10 ***
## 16: 57 9.9e-10 ***
## [1] 56.6
## [1] 2.19

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.074 34   0.0093 **
##  2:      0.09375          0.110 36     0.011 *
##  3:      0.15625          0.094 42     0.011 *
##  4:      0.21875          0.130 40 0.00017 ***
##  5:      0.28125          0.110 38   0.0028 **
##  6:      0.34375          0.120 38 0.00027 ***
##  7:      0.40625          0.077 40     0.022 *
##  8:      0.46875          0.031 38     0.22 :(
##  9:      0.53125         -0.031 37     0.35 :(
## 10:      0.59375         -0.058 40      0.09 .
## 11:      0.65625         -0.098 36     0.027 *
## 12:      0.71875         -0.180 37 1.5e-05 ***
## 13:      0.78125         -0.180 38 6.7e-05 ***
## 14:      0.84375         -0.240 25 0.00013 ***
## 15:      0.90625         -0.260 29   1e-05 ***
## 16:      0.96875         -0.180 19    0.003 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 34   0.0093 **
##  2: 36     0.011 *
##  3: 42     0.011 *
##  4: 40 0.00017 ***
##  5: 38   0.0028 **
##  6: 38 0.00027 ***
##  7: 40     0.022 *
##  8: 38     0.22 :(
##  9: 37     0.35 :(
## 10: 40      0.09 .
## 11: 36     0.027 *
## 12: 37 1.5e-05 ***
## 13: 38 6.7e-05 ***
## 14: 25 0.00013 ***
## 15: 29   1e-05 ***
## 16: 19    0.003 **
## [1] 35.4
## [1] 6.11

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.140 28   0.0017 **
##  2:      0.09375          0.170 32 0.00031 ***
##  3:      0.15625          0.120 29   0.0086 **
##  4:      0.21875          0.130 36   0.0031 **
##  5:      0.28125          0.094 33     0.12 :(
##  6:      0.34375          0.110 36     0.033 *
##  7:      0.40625          0.036 36     0.55 :(
##  8:      0.46875         -0.010 34     0.78 :(
##  9:      0.53125         -0.029 35     0.71 :(
## 10:      0.59375         -0.054 33     0.42 :(
## 11:      0.65625         -0.160 36 0.00019 ***
## 12:      0.71875         -0.110 37   0.0026 **
## 13:      0.78125         -0.150 37 0.00012 ***
## 14:      0.84375         -0.210 36   1e-05 ***
## 15:      0.90625         -0.230 33 2.8e-06 ***
## 16:      0.96875         -0.150 31 2.6e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 28   0.0017 **
##  2: 32 0.00031 ***
##  3: 29   0.0086 **
##  4: 36   0.0031 **
##  5: 33     0.12 :(
##  6: 36     0.033 *
##  7: 36     0.55 :(
##  8: 34     0.78 :(
##  9: 35     0.71 :(
## 10: 33     0.42 :(
## 11: 36 0.00019 ***
## 12: 37   0.0026 **
## 13: 37 0.00012 ***
## 14: 36   1e-05 ***
## 15: 33 2.8e-06 ***
## 16: 31 2.6e-05 ***
## [1] 33.9
## [1] 2.78

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375          0.081 10     0.21 :(
##  3:      0.15625          0.190 12     0.036 *
##  4:      0.21875          0.031 12     0.29 :(
##  5:      0.28125          0.220 13   0.0061 **
##  6:      0.34375          0.160 12   0.0044 **
##  7:      0.40625          0.160 14     0.059 .
##  8:      0.46875          0.031 16     0.15 :(
##  9:      0.53125         -0.031 16     0.029 *
## 10:      0.59375          0.021 16      0.9 :(
## 11:      0.65625         -0.031 16     0.62 :(
## 12:      0.71875         -0.062 16     0.11 :(
## 13:      0.78125         -0.180 18   0.0076 **
## 14:      0.84375         -0.240 20 0.00045 ***
## 15:      0.90625         -0.230 20 0.00022 ***
## 16:      0.96875         -0.310 20 9.3e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 10     0.21 :(
##  2: 12     0.036 *
##  3: 12     0.29 :(
##  4: 13   0.0061 **
##  5: 12   0.0044 **
##  6: 14     0.059 .
##  7: 16     0.15 :(
##  8: 16     0.029 *
##  9: 16      0.9 :(
## 10: 16     0.62 :(
## 11: 16     0.11 :(
## 12: 18   0.0076 **
## 13: 20 0.00045 ***
## 14: 20 0.00022 ***
## 15: 20 9.3e-05 ***
## [1] 15.4
## [1] 3.2
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

Motor task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375          0.120  8     0.44 :(
##  3:      0.15625          0.094 26     0.44 :(
##  4:      0.21875          0.067 40     0.052 .
##  5:      0.28125          0.069 45      0.09 .
##  6:      0.34375          0.110 47     0.013 *
##  7:      0.40625          0.064 49     0.091 .
##  8:      0.46875          0.048 49     0.045 *
##  9:      0.53125          0.019 51     0.56 :(
## 10:      0.59375         -0.044 51     0.41 :(
## 11:      0.65625         -0.090 53   0.0052 **
## 12:      0.71875         -0.069 51   0.0016 **
## 13:      0.78125         -0.110 44 0.00056 ***
## 14:      0.84375         -0.170 27   0.0029 **
## 15:      0.90625         -0.210 14   0.0094 **
## 16:      0.96875         -0.270  6     0.056 .
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  8     0.44 :(
##  2: 26     0.44 :(
##  3: 40     0.052 .
##  4: 45      0.09 .
##  5: 47     0.013 *
##  6: 49     0.091 .
##  7: 49     0.045 *
##  8: 51     0.56 :(
##  9: 51     0.41 :(
## 10: 53   0.0052 **
## 11: 51   0.0016 **
## 12: 44 0.00056 ***
## 13: 27   0.0029 **
## 14: 14   0.0094 **
## 15:  6     0.056 .
## [1] 37.4
## [1] 16.7
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375         0.1200  8   0.44 :(
##  3:      0.15625         0.0770 24   0.52 :(
##  4:      0.21875         0.0680 26   0.13 :(
##  5:      0.28125         0.1100 25   0.029 *
##  6:      0.34375         0.1100 26 0.0021 **
##  7:      0.40625         0.0940 25   0.036 *
##  8:      0.46875         0.1100 24 0.0068 **
##  9:      0.53125         0.0690 23   0.37 :(
## 10:      0.59375         0.0410 24   0.62 :(
## 11:      0.65625        -0.0063 25    0.4 :(
## 12:      0.71875        -0.0690 22   0.054 .
## 13:      0.78125        -0.0810 15   0.042 *
## 14:      0.84375             NA  0        NA
## 15:      0.90625             NA  0        NA
## 16:      0.96875             NA  0        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  8   0.44 :(
##  2: 24   0.52 :(
##  3: 26   0.13 :(
##  4: 25   0.029 *
##  5: 26 0.0021 **
##  6: 25   0.036 *
##  7: 24 0.0068 **
##  8: 23   0.37 :(
##  9: 24   0.62 :(
## 10: 25    0.4 :(
## 11: 22   0.054 .
## 12: 15   0.042 *
## [1] 22.2
## [1] 5.36
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  0        NA
##  3:      0.15625          0.290  2      1 :(
##  4:      0.21875          0.073 14   0.23 :(
##  5:      0.28125          0.035 20   0.64 :(
##  6:      0.34375          0.044 20   0.81 :(
##  7:      0.40625          0.014 22   0.95 :(
##  8:      0.46875         -0.019 21   0.68 :(
##  9:      0.53125          0.019 21   0.53 :(
## 10:      0.59375         -0.094 21    0.06 .
## 11:      0.65625         -0.160 21 0.0065 **
## 12:      0.71875         -0.069 22   0.066 .
## 13:      0.78125         -0.081 21    0.07 .
## 14:      0.84375         -0.180 19   0.016 *
## 15:      0.90625         -0.230  6   0.093 .
## 16:      0.96875             NA  0        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  2      1 :(
##  2: 14   0.23 :(
##  3: 20   0.64 :(
##  4: 20   0.81 :(
##  5: 22   0.95 :(
##  6: 21   0.68 :(
##  7: 21   0.53 :(
##  8: 21    0.06 .
##  9: 21 0.0065 **
## 10: 22   0.066 .
## 11: 21    0.07 .
## 12: 19   0.016 *
## 13:  6   0.093 .
## [1] 17.7
## [1] 6.46
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 0      NA
##  2:      0.09375             NA 0      NA
##  3:      0.15625             NA 0      NA
##  4:      0.21875             NA 0      NA
##  5:      0.28125             NA 0      NA
##  6:      0.34375             NA 1      NA
##  7:      0.40625          0.190 2  0.5 :(
##  8:      0.46875             NA 4      NA
##  9:      0.53125         -0.031 7 0.19 :(
## 10:      0.59375         -0.044 6 0.52 :(
## 11:      0.65625         -0.160 7 0.33 :(
## 12:      0.71875         -0.100 7 0.14 :(
## 13:      0.78125         -0.180 8 0.028 *
## 14:      0.84375         -0.160 8  0.1 :(
## 15:      0.90625         -0.210 8 0.055 .
## 16:      0.96875         -0.270 6 0.056 .
## [1] "mean and sd of nb players per bin"
##    nb    pval
## 1:  2  0.5 :(
## 2:  7 0.19 :(
## 3:  6 0.52 :(
## 4:  7 0.33 :(
## 5:  7 0.14 :(
## 6:  8 0.028 *
## 7:  8  0.1 :(
## 8:  8 0.055 .
## 9:  6 0.056 .
## [1] 6.56
## [1] 1.88
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.085 44   0.0094 **
##  2:      0.09375          0.140 53   0.0041 **
##  3:      0.15625          0.094 48     0.081 .
##  4:      0.21875          0.048 40     0.069 .
##  5:      0.28125          0.019 38     0.59 :(
##  6:      0.34375          0.056 36     0.43 :(
##  7:      0.40625         -0.031 37     0.42 :(
##  8:      0.46875         -0.120 37     0.041 *
##  9:      0.53125         -0.210 30 0.00093 ***
## 10:      0.59375         -0.094 33     0.014 *
## 11:      0.65625         -0.160 34 8.1e-05 ***
## 12:      0.71875         -0.220 34 0.00014 ***
## 13:      0.78125         -0.280 38 7.5e-07 ***
## 14:      0.84375         -0.270 45 8.4e-07 ***
## 15:      0.90625         -0.230 53 3.5e-09 ***
## 16:      0.96875         -0.150 56   4e-08 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 44   0.0094 **
##  2: 53   0.0041 **
##  3: 48     0.081 .
##  4: 40     0.069 .
##  5: 38     0.59 :(
##  6: 36     0.43 :(
##  7: 37     0.42 :(
##  8: 37     0.041 *
##  9: 30 0.00093 ***
## 10: 33     0.014 *
## 11: 34 8.1e-05 ***
## 12: 34 0.00014 ***
## 13: 38 7.5e-07 ***
## 14: 45 8.4e-07 ***
## 15: 53 3.5e-09 ***
## 16: 56   4e-08 ***
## [1] 41
## [1] 7.94

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.084 19      0.1 :(
##  2:      0.09375          0.031 18     0.72 :(
##  3:      0.15625          0.094 17     0.31 :(
##  4:      0.21875          0.081 10     0.26 :(
##  5:      0.28125          0.052 16     0.45 :(
##  6:      0.34375         -0.044 12     0.14 :(
##  7:      0.40625         -0.160 12     0.024 *
##  8:      0.46875         -0.170 15     0.018 *
##  9:      0.53125         -0.280 11     0.018 *
## 10:      0.59375         -0.290 12      0.01 *
## 11:      0.65625         -0.280 12   0.0022 **
## 12:      0.71875         -0.340 11   0.0033 **
## 13:      0.78125         -0.280 12   0.0081 **
## 14:      0.84375         -0.340 13   0.0039 **
## 15:      0.90625         -0.230 18 0.00048 ***
## 16:      0.96875         -0.170 19   0.0065 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 19      0.1 :(
##  2: 18     0.72 :(
##  3: 17     0.31 :(
##  4: 10     0.26 :(
##  5: 16     0.45 :(
##  6: 12     0.14 :(
##  7: 12     0.024 *
##  8: 15     0.018 *
##  9: 11     0.018 *
## 10: 12      0.01 *
## 11: 12   0.0022 **
## 12: 11   0.0033 **
## 13: 12   0.0081 **
## 14: 13   0.0039 **
## 15: 18 0.00048 ***
## 16: 19   0.0065 **
## [1] 14.2
## [1] 3.17

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0850 25     0.052 .
##  2:      0.09375         0.1600 27     0.012 *
##  3:      0.15625        -0.0062 21     0.97 :(
##  4:      0.21875         0.0310 22     0.24 :(
##  5:      0.28125        -0.0310 15     0.71 :(
##  6:      0.34375         0.0730 19     0.059 .
##  7:      0.40625         0.0380 20     0.87 :(
##  8:      0.46875        -0.0440 17     0.81 :(
##  9:      0.53125        -0.1100 15     0.091 .
## 10:      0.59375        -0.0690 16     0.45 :(
## 11:      0.65625        -0.1600 17     0.017 *
## 12:      0.71875        -0.1200 16      0.03 *
## 13:      0.78125        -0.2300 21 0.00041 ***
## 14:      0.84375        -0.2400 24 0.00092 ***
## 15:      0.90625        -0.2100 27   5e-05 ***
## 16:      0.96875        -0.0800 27 0.00018 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 25     0.052 .
##  2: 27     0.012 *
##  3: 21     0.97 :(
##  4: 22     0.24 :(
##  5: 15     0.71 :(
##  6: 19     0.059 .
##  7: 20     0.87 :(
##  8: 17     0.81 :(
##  9: 15     0.091 .
## 10: 16     0.45 :(
## 11: 17     0.017 *
## 12: 16      0.03 *
## 13: 21 0.00041 ***
## 14: 24 0.00092 ***
## 15: 27   5e-05 ***
## 16: 27 0.00018 ***
## [1] 20.6
## [1] 4.4

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375          0.140  8    0.1 :(
##  3:      0.15625          0.190 10    0.04 *
##  4:      0.21875          0.070  8   0.44 :(
##  5:      0.28125          0.094  7   0.44 :(
##  6:      0.34375          0.081  5   0.18 :(
##  7:      0.40625          0.110  5   0.44 :(
##  8:      0.46875         -0.120  5   0.78 :(
##  9:      0.53125         -0.260  4   0.12 :(
## 10:      0.59375         -0.094  5   0.58 :(
## 11:      0.65625         -0.160  5   0.41 :(
## 12:      0.71875         -0.074  7   0.55 :(
## 13:      0.78125         -0.280  5   0.054 .
## 14:      0.84375         -0.220  8   0.041 *
## 15:      0.90625         -0.290  8   0.014 *
## 16:      0.96875         -0.240 10 0.0059 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  8    0.1 :(
##  2: 10    0.04 *
##  3:  8   0.44 :(
##  4:  7   0.44 :(
##  5:  5   0.18 :(
##  6:  5   0.44 :(
##  7:  5   0.78 :(
##  8:  4   0.12 :(
##  9:  5   0.58 :(
## 10:  5   0.41 :(
## 11:  7   0.55 :(
## 12:  5   0.054 .
## 13:  8   0.041 *
## 14:  8   0.014 *
## 15: 10 0.0059 **
## [1] 6.67
## [1] 1.95
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

Logical task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.089 35     0.017 *
##  2:      0.09375          0.160 40 5.2e-05 ***
##  3:      0.15625          0.150 40 0.00025 ***
##  4:      0.21875          0.230 42 9.5e-06 ***
##  5:      0.28125          0.220 34 0.00028 ***
##  6:      0.34375          0.160 39 5.5e-05 ***
##  7:      0.40625          0.094 44     0.011 *
##  8:      0.46875          0.031 39     0.024 *
##  9:      0.53125         -0.031 37     0.21 :(
## 10:      0.59375         -0.019 41     0.77 :(
## 11:      0.65625         -0.018 39     0.68 :(
## 12:      0.71875         -0.100 38    0.002 **
## 13:      0.78125         -0.160 43 9.5e-05 ***
## 14:      0.84375         -0.220 41 6.5e-07 ***
## 15:      0.90625         -0.260 40 3.4e-07 ***
## 16:      0.96875         -0.340 25 1.4e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 35     0.017 *
##  2: 40 5.2e-05 ***
##  3: 40 0.00025 ***
##  4: 42 9.5e-06 ***
##  5: 34 0.00028 ***
##  6: 39 5.5e-05 ***
##  7: 44     0.011 *
##  8: 39     0.024 *
##  9: 37     0.21 :(
## 10: 41     0.77 :(
## 11: 39     0.68 :(
## 12: 38    0.002 **
## 13: 43 9.5e-05 ***
## 14: 41 6.5e-07 ***
## 15: 40 3.4e-07 ***
## 16: 25 1.4e-05 ***
## [1] 38.6
## [1] 4.47

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125          0.050 26   0.071 .
##  2:      0.09375          0.110 26  0.007 **
##  3:      0.15625          0.110 24   0.027 *
##  4:      0.21875          0.200 24 0.0014 **
##  5:      0.28125          0.140 17   0.13 :(
##  6:      0.34375          0.160 21   0.036 *
##  7:      0.40625          0.120 22   0.085 .
##  8:      0.46875          0.031 20   0.15 :(
##  9:      0.53125         -0.031 18   0.27 :(
## 10:      0.59375         -0.094 21   0.19 :(
## 11:      0.65625         -0.056 17   0.57 :(
## 12:      0.71875         -0.120 18   0.026 *
## 13:      0.78125         -0.160 21 0.0081 **
## 14:      0.84375         -0.220 18 0.0018 **
## 15:      0.90625         -0.310 15 0.0024 **
## 16:      0.96875             NA  1        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1: 26   0.071 .
##  2: 26  0.007 **
##  3: 24   0.027 *
##  4: 24 0.0014 **
##  5: 17   0.13 :(
##  6: 21   0.036 *
##  7: 22   0.085 .
##  8: 20   0.15 :(
##  9: 18   0.27 :(
## 10: 21   0.19 :(
## 11: 17   0.57 :(
## 12: 18   0.026 *
## 13: 21 0.0081 **
## 14: 18 0.0018 **
## 15: 15 0.0024 **
## [1] 20.5
## [1] 3.4
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125          0.140  9   0.15 :(
##  2:      0.09375          0.360 12 0.0031 **
##  3:      0.15625          0.340 13 0.0026 **
##  4:      0.21875          0.330 13 0.0031 **
##  5:      0.28125          0.220 10 0.0056 **
##  6:      0.34375          0.160 10 0.0067 **
##  7:      0.40625          0.094 13   0.35 :(
##  8:      0.46875          0.031 11   0.049 *
##  9:      0.53125         -0.031 10   0.75 :(
## 10:      0.59375          0.031 10   0.36 :(
## 11:      0.65625         -0.110 12   0.12 :(
## 12:      0.71875         -0.220 12   0.053 .
## 13:      0.78125         -0.240 13 0.0039 **
## 14:      0.84375         -0.220 13 0.0026 **
## 15:      0.90625         -0.250 13 0.0032 **
## 16:      0.96875         -0.340 12 0.0031 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  9   0.15 :(
##  2: 12 0.0031 **
##  3: 13 0.0026 **
##  4: 13 0.0031 **
##  5: 10 0.0056 **
##  6: 10 0.0067 **
##  7: 13   0.35 :(
##  8: 11   0.049 *
##  9: 10   0.75 :(
## 10: 10   0.36 :(
## 11: 12   0.12 :(
## 12: 12   0.053 .
## 13: 13 0.0039 **
## 14: 13 0.0026 **
## 15: 13 0.0032 **
## 16: 12 0.0031 **
## [1] 11.6
## [1] 1.41

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  2        NA
##  3:      0.15625             NA  3        NA
##  4:      0.21875          0.031  5   0.59 :(
##  5:      0.28125          0.450  7   0.034 *
##  6:      0.34375          0.270  8   0.019 *
##  7:      0.40625          0.240  9   0.096 .
##  8:      0.46875          0.120  8   0.53 :(
##  9:      0.53125         -0.031  9   0.55 :(
## 10:      0.59375          0.031 10   0.68 :(
## 11:      0.65625          0.069 10   0.22 :(
## 12:      0.71875         -0.056  8   0.44 :(
## 13:      0.78125         -0.031  9   0.55 :(
## 14:      0.84375         -0.220 10   0.014 *
## 15:      0.90625         -0.240 12 0.0052 **
## 16:      0.96875         -0.350 12 0.0025 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  5   0.59 :(
##  2:  7   0.034 *
##  3:  8   0.019 *
##  4:  9   0.096 .
##  5:  8   0.53 :(
##  6:  9   0.55 :(
##  7: 10   0.68 :(
##  8: 10   0.22 :(
##  9:  8   0.44 :(
## 10:  9   0.55 :(
## 11: 10   0.014 *
## 12: 12 0.0052 **
## 13: 12 0.0025 **
## [1] 9
## [1] 1.91
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_errorbar).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.70319  -0.16766   0.00799   0.17682   0.64502  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.17379    0.01995   8.711   <2e-16 ***
## timeNorm     0.00431    0.02101   0.205    0.837    
## obj.diff    -0.37273    0.02619 -14.234   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05419176)
## 
##     Null deviance: 99.193  on 1623  degrees of freedom
## Residual deviance: 87.845  on 1621  degrees of freedom
## AIC: -120.62
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.77933  -0.20089  -0.03724   0.24111   0.77727  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.10795    0.01735   6.220  6.3e-10 ***
## timeNorm     0.03878    0.02320   1.672   0.0948 .  
## obj.diff    -0.36404    0.01795 -20.278  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06798547)
## 
##     Null deviance: 138.38  on 1623  degrees of freedom
## Residual deviance: 110.20  on 1621  degrees of freedom
## AIC: 247.65
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74305  -0.21400  -0.02148   0.20096   0.71922  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.20615    0.02036  10.127  < 2e-16 ***
## timeNorm     0.06739    0.02531   2.662  0.00785 ** 
## obj.diff    -0.51720    0.02162 -23.927  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07044787)
## 
##     Null deviance: 151.98  on 1507  degrees of freedom
## Residual deviance: 106.02  on 1505  degrees of freedom
## AIC: 283.97
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean    error.diff   n    pval
##  1:      1.5      0.5348214     0.6008109 -0.0627956484 112 0.021 *
##  2:      4.5      0.5291667     0.5714407 -0.0363491910 168 0.071 .
##  3:      7.5      0.5071429     0.5416953 -0.0317522384 168 0.12 :(
##  4:     10.5      0.5339286     0.5401276  0.0027661467 168 0.89 :(
##  5:     13.5      0.5071429     0.5174551 -0.0066784780 168 0.74 :(
##  6:     16.5      0.5232143     0.5305272 -0.0054376495 168 0.78 :(
##  7:     19.5      0.4976190     0.5315528 -0.0349803686 168 0.062 .
##  8:     22.5      0.4779762     0.4897264 -0.0103383643 168 0.64 :(
##  9:     25.5      0.4797619     0.4805683  0.0009212402 168 0.95 :(
## 10:     28.5      0.4642857     0.4572889  0.0071690193 168 0.72 :(
##     time    error.diff shapes
##  1:  1.5 -0.0627956484     24
##  2:  4.5 -0.0363491910     16
##  3:  7.5 -0.0317522384     16
##  4: 10.5  0.0027661467     16
##  5: 13.5 -0.0066784780     16
##  6: 16.5 -0.0054376495     16
##  7: 19.5 -0.0349803686     16
##  8: 22.5 -0.0103383643     16
##  9: 25.5  0.0009212402     16
## 10: 28.5  0.0071690193     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4696429     0.5941293 -0.13598057 112 2.7e-05 ***
##  2:      4.5      0.5125000     0.6104788 -0.08536726 168 5.7e-06 ***
##  3:      7.5      0.4666667     0.5299114 -0.06382281 168   0.0032 **
##  4:     10.5      0.5148810     0.5824635 -0.06568890 168   0.0015 **
##  5:     13.5      0.4773810     0.5656294 -0.08101223 168 1.6e-05 ***
##  6:     16.5      0.4345238     0.5333505 -0.10807690 168 6.4e-06 ***
##  7:     19.5      0.4875000     0.5641391 -0.06577197 168 0.00038 ***
##  8:     22.5      0.4976190     0.5656705 -0.05806955 168    0.003 **
##  9:     25.5      0.5392857     0.5874740 -0.03434793 168      0.06 .
## 10:     28.5      0.5017857     0.5711020 -0.06820755 168   0.0022 **
##     time  error.diff shapes
##  1:  1.5 -0.13598057     24
##  2:  4.5 -0.08536726     24
##  3:  7.5 -0.06382281     24
##  4: 10.5 -0.06568890     24
##  5: 13.5 -0.08101223     24
##  6: 16.5 -0.10807690     24
##  7: 19.5 -0.06577197     24
##  8: 22.5 -0.05806955     24
##  9: 25.5 -0.03434793     16
## 10: 28.5 -0.06820755     24

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.4355769     0.5969130 -0.167868594 104 3.2e-06 ***
##  2:      4.5      0.5089744     0.6297636 -0.133783305 156 3.6e-06 ***
##  3:      7.5      0.5102564     0.5544687 -0.055654906 156     0.036 *
##  4:     10.5      0.5224359     0.5229882 -0.002890341 156     0.89 :(
##  5:     13.5      0.5173077     0.5312208 -0.020469231 156     0.44 :(
##  6:     16.5      0.5102564     0.5008164  0.003037161 156     0.91 :(
##  7:     19.5      0.4576923     0.4456698  0.001732469 156     0.95 :(
##  8:     22.5      0.4211538     0.4198655 -0.005262489 156     0.84 :(
##  9:     25.5      0.4576923     0.3963862  0.067707055 156     0.015 *
## 10:     28.5      0.4435897     0.3637653  0.061919707 156     0.014 *
##     time   error.diff shapes
##  1:  1.5 -0.167868594     24
##  2:  4.5 -0.133783305     24
##  3:  7.5 -0.055654906     24
##  4: 10.5 -0.002890341     16
##  5: 13.5 -0.020469231     16
##  6: 16.5  0.003037161     16
##  7: 19.5  0.001732469     16
##  8: 22.5 -0.005262489     16
##  9: 25.5  0.067707055     24
## 10: 28.5  0.061919707     24

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74490  -0.17535  -0.06458   0.23455   0.57334  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.28603    0.03171   9.021  < 2e-16 ***
## timeNorm     0.08066    0.03043   2.650  0.00819 ** 
## obj.diff    -0.61786    0.03250 -19.010  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06159923)
## 
##     Null deviance: 77.326  on 869  degrees of freedom
## Residual deviance: 53.407  on 867  degrees of freedom
## AIC: 49.166
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74652  -0.21532   0.00706   0.22454   0.74696  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.18368    0.01831  10.033   <2e-16 ***
## timeNorm     0.03187    0.02201   1.448    0.148    
## obj.diff    -0.42661    0.02054 -20.770   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06803624)
## 
##     Null deviance: 154.38  on 1826  degrees of freedom
## Residual deviance: 124.10  on 1824  degrees of freedom
## AIC: 279.34
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7579  -0.1902  -0.0057   0.2034   0.7204  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.13887    0.01539   9.024   <2e-16 ***
## timeNorm     0.03309    0.01996   1.658   0.0974 .  
## obj.diff    -0.37629    0.01927 -19.528   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06202091)
## 
##     Null deviance: 152.70  on 2058  degrees of freedom
## Residual deviance: 127.51  on 2056  degrees of freedom
## AIC: 123.58
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5550000     0.7875694 -0.24249236 60   1e-07 ***
##  2:      4.5      0.5711111     0.7762393 -0.22395064 90 8.2e-08 ***
##  3:      7.5      0.6122222     0.7666760 -0.17059578 90 3.5e-06 ***
##  4:     10.5      0.6355556     0.7322887 -0.10318972 90   0.0031 **
##  5:     13.5      0.6277778     0.7646042 -0.16703095 90 1.3e-05 ***
##  6:     16.5      0.6155556     0.7327936 -0.13630021 90 0.00022 ***
##  7:     19.5      0.6311111     0.7168309 -0.09544489 90   0.0014 **
##  8:     22.5      0.6188889     0.7273818 -0.11152008 90   0.0023 **
##  9:     25.5      0.6011111     0.6908376 -0.08505521 90     0.019 *
## 10:     28.5      0.6100000     0.6655627 -0.05175360 90     0.12 :(
##     time  error.diff shapes
##  1:  1.5 -0.24249236     24
##  2:  4.5 -0.22395064     24
##  3:  7.5 -0.17059578     24
##  4: 10.5 -0.10318972     24
##  5: 13.5 -0.16703095     24
##  6: 16.5 -0.13630021     24
##  7: 19.5 -0.09544489     24
##  8: 22.5 -0.11152008     24
##  9: 25.5 -0.08505521     24
## 10: 28.5 -0.05175360     16

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.5055556     0.6004377 -0.096794693 126   0.0012 **
##  2:      4.5      0.5693122     0.6650662 -0.091152068 189 1.2e-05 ***
##  3:      7.5      0.5095238     0.5257523 -0.023100429 189     0.28 :(
##  4:     10.5      0.5455026     0.5759928 -0.028141123 189     0.23 :(
##  5:     13.5      0.5301587     0.5697923 -0.037004401 189     0.049 *
##  6:     16.5      0.5164021     0.5456342 -0.033848317 189     0.11 :(
##  7:     19.5      0.4957672     0.5590654 -0.065529105 189    0.002 **
##  8:     22.5      0.4862434     0.5124916 -0.031883747 189     0.14 :(
##  9:     25.5      0.5333333     0.5246238  0.003500624 189     0.88 :(
## 10:     28.5      0.5074074     0.5116642 -0.013045432 189     0.56 :(
##     time   error.diff shapes
##  1:  1.5 -0.096794693     24
##  2:  4.5 -0.091152068     24
##  3:  7.5 -0.023100429     16
##  4: 10.5 -0.028141123     16
##  5: 13.5 -0.037004401     24
##  6: 16.5 -0.033848317     16
##  7: 19.5 -0.065529105     24
##  8: 22.5 -0.031883747     16
##  9: 25.5  0.003500624     16
## 10: 28.5 -0.013045432     16

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n      pval
##  1:      1.5      0.4281690     0.5141051 -0.080402452 142 0.0021 **
##  2:      4.5      0.4478873     0.4753359 -0.027143114 213   0.16 :(
##  3:      7.5      0.4309859     0.4608404 -0.028485150 213   0.14 :(
##  4:     10.5      0.4572770     0.4479476  0.013162796 213   0.49 :(
##  5:     13.5      0.4197183     0.4146644  0.010568298 213    0.6 :(
##  6:     16.5      0.4107981     0.4121246 -0.002777196 213    0.9 :(
##  7:     19.5      0.4056338     0.3916553  0.010544404 213   0.59 :(
##  8:     22.5      0.3849765     0.3778424  0.005899965 213   0.72 :(
##  9:     25.5      0.4117371     0.3752961  0.035408215 213   0.038 *
## 10:     28.5      0.3788732     0.3423094  0.026264248 213   0.17 :(
##     time   error.diff shapes
##  1:  1.5 -0.080402452     24
##  2:  4.5 -0.027143114     16
##  3:  7.5 -0.028485150     16
##  4: 10.5  0.013162796     16
##  5: 13.5  0.010568298     16
##  6: 16.5 -0.002777196     16
##  7: 19.5  0.010544404     16
##  8: 22.5  0.005899965     16
##  9: 25.5  0.035408215     24
## 10: 28.5  0.026264248     16

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.65062  -0.16552  -0.07705   0.21881   0.38387  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.29158    0.07860   3.710  0.00026 ***
## timeNorm     0.04078    0.04734   0.861  0.38990    
## obj.diff    -0.58583    0.08967  -6.533 4.12e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.03968196)
## 
##     Null deviance: 10.9054  on 231  degrees of freedom
## Residual deviance:  9.0872  on 229  degrees of freedom
## AIC: -85.263
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.6250000     0.8544830 -0.23067342 16   0.0013 **
##  2:      4.5      0.6375000     0.7995145 -0.16957820 24   0.0048 **
##  3:      7.5      0.6208333     0.7551085 -0.13379284 24     0.012 *
##  4:     10.5      0.6375000     0.7836615 -0.15718140 24   0.0079 **
##  5:     13.5      0.6250000     0.8240112 -0.20576489 24 6.4e-05 ***
##  6:     16.5      0.6375000     0.7818411 -0.15147782 24     0.027 *
##  7:     19.5      0.6541667     0.7263256 -0.07096924 24     0.13 :(
##  8:     22.5      0.6458333     0.7654436 -0.12523757 24     0.046 *
##  9:     25.5      0.6583333     0.7908307 -0.13301969 24   0.0072 **
## 10:     28.5      0.6166667     0.7394768 -0.11097038 24     0.039 *
##     time  error.diff shapes
##  1:  1.5 -0.23067342     24
##  2:  4.5 -0.16957820     24
##  3:  7.5 -0.13379284     24
##  4: 10.5 -0.15718140     24
##  5: 13.5 -0.20576489     24
##  6: 16.5 -0.15147782     24
##  7: 19.5 -0.07096924     16
##  8: 22.5 -0.12523757     24
##  9: 25.5 -0.13301969     24
## 10: 28.5 -0.11097038     24

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.68934  -0.16575   0.00973   0.19104   0.67014  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.157587   0.040226   3.918 9.92e-05 ***
## timeNorm    -0.008747   0.037508  -0.233    0.816    
## obj.diff    -0.364236   0.054058  -6.738 3.61e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06744585)
## 
##     Null deviance: 45.961  on 637  degrees of freedom
## Residual deviance: 42.828  on 635  degrees of freedom
## AIC: 95.236
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.5204545     0.6251419 -0.096882522 44   0.034 *
##  2:      4.5      0.5454545     0.6224524 -0.069888163 66   0.042 *
##  3:      7.5      0.5212121     0.5482212 -0.022392160 66   0.54 :(
##  4:     10.5      0.5257576     0.5744464 -0.036347555 66   0.35 :(
##  5:     13.5      0.5348485     0.5455378 -0.006192686 66   0.85 :(
##  6:     16.5      0.5272727     0.5560045 -0.033252815 66   0.35 :(
##  7:     19.5      0.4712121     0.5704673 -0.107605826 66 0.0013 **
##  8:     22.5      0.4439394     0.5060978 -0.066259279 66   0.063 .
##  9:     25.5      0.4787879     0.4999714 -0.024063555 66   0.54 :(
## 10:     28.5      0.4787879     0.5016324 -0.029290994 66   0.31 :(
##     time   error.diff shapes
##  1:  1.5 -0.096882522     24
##  2:  4.5 -0.069888163     24
##  3:  7.5 -0.022392160     16
##  4: 10.5 -0.036347555     16
##  5: 13.5 -0.006192686     16
##  6: 16.5 -0.033252815     16
##  7: 19.5 -0.107605826     24
##  8: 22.5 -0.066259279     16
##  9: 25.5 -0.024063555     16
## 10: 28.5 -0.029290994     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.61934  -0.16018   0.01038   0.17385   0.53652  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.11181    0.02651   4.217 2.78e-05 ***
## timeNorm     0.02800    0.02850   0.983    0.326    
## obj.diff    -0.19693    0.04083  -4.823 1.71e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.04519554)
## 
##     Null deviance: 35.197  on 753  degrees of freedom
## Residual deviance: 33.942  on 751  degrees of freedom
## AIC: -190.2
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.5192308     0.5021701  0.020553607 52   0.56 :(
##  2:      4.5      0.4820513     0.4581003  0.028602593 78   0.28 :(
##  3:      7.5      0.4602564     0.4705078 -0.007131425 78   0.76 :(
##  4:     10.5      0.5089744     0.4361551  0.085310624 78 0.0021 **
##  5:     13.5      0.4474359     0.3993679  0.055711847 78   0.043 *
##  6:     16.5      0.4846154     0.4316421  0.056312672 78   0.036 *
##  7:     19.5      0.4717949     0.4386951  0.030866623 78   0.22 :(
##  8:     22.5      0.4551282     0.3910376  0.068335238 78   0.013 *
##  9:     25.5      0.4256410     0.3686849  0.059334781 78   0.014 *
## 10:     28.5      0.4051282     0.3329405  0.069444110 78 0.0055 **
##     time   error.diff shapes
##  1:  1.5  0.020553607     16
##  2:  4.5  0.028602593     16
##  3:  7.5 -0.007131425     16
##  4: 10.5  0.085310624     24
##  5: 13.5  0.055711847     24
##  6: 16.5  0.056312672     24
##  7: 19.5  0.030866623     16
##  8: 22.5  0.068335238     24
##  9: 25.5  0.059334781     24
## 10: 28.5  0.069444110     24

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.73821  -0.20661  -0.03259   0.20583   0.62369  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.22002    0.04425   4.972 1.14e-06 ***
## timeNorm     0.03942    0.05278   0.747    0.456    
## obj.diff    -0.51835    0.04440 -11.675  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06277719)
## 
##     Null deviance: 26.645  on 289  degrees of freedom
## Residual deviance: 18.017  on 287  degrees of freedom
## AIC: 25.201
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.5200000     0.6390463 -0.13867440 20   0.11 :(
##  2:      4.5      0.5233333     0.6686706 -0.14681000 30   0.023 *
##  3:      7.5      0.5600000     0.7179520 -0.16882939 30 0.0047 **
##  4:     10.5      0.6166667     0.7022945 -0.09167106 30   0.13 :(
##  5:     13.5      0.6300000     0.7355270 -0.09696383 30   0.047 *
##  6:     16.5      0.5033333     0.6316433 -0.17116360 30   0.026 *
##  7:     19.5      0.5666667     0.6735104 -0.14469214 30   0.061 .
##  8:     22.5      0.6766667     0.7285240 -0.04571003 30   0.52 :(
##  9:     25.5      0.5200000     0.6387517 -0.10658266 30    0.07 .
## 10:     28.5      0.5400000     0.6238117 -0.06667086 30   0.26 :(
##     time  error.diff shapes
##  1:  1.5 -0.13867440     16
##  2:  4.5 -0.14681000     24
##  3:  7.5 -0.16882939     24
##  4: 10.5 -0.09167106     16
##  5: 13.5 -0.09696383     24
##  6: 16.5 -0.17116360     24
##  7: 19.5 -0.14469214     16
##  8: 22.5 -0.04571003     16
##  9: 25.5 -0.10658266     16
## 10: 28.5 -0.06667086     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.72208  -0.20591   0.01923   0.20060   0.76828  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.11070    0.02506   4.417 1.14e-05 ***
## timeNorm     0.03910    0.03334   1.173    0.241    
## obj.diff    -0.31529    0.02603 -12.114  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06768712)
## 
##     Null deviance: 62.835  on 782  degrees of freedom
## Residual deviance: 52.796  on 780  degrees of freedom
## AIC: 118.54
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.5185185     0.5856813 -0.077995802 54   0.068 .
##  2:      4.5      0.5839506     0.6540986 -0.051214048 81 0.0094 **
##  3:      7.5      0.4753086     0.4885319 -0.021685483 81    0.5 :(
##  4:     10.5      0.5246914     0.5978029 -0.068227022 81   0.038 *
##  5:     13.5      0.4975309     0.5802643 -0.074552535 81 0.0055 **
##  6:     16.5      0.4765432     0.5255582 -0.048653420 81   0.12 :(
##  7:     19.5      0.5222222     0.5760814 -0.029460170 81   0.19 :(
##  8:     22.5      0.4962963     0.5370262 -0.035337710 81   0.12 :(
##  9:     25.5      0.5827160     0.5877937 -0.003735285 81   0.87 :(
## 10:     28.5      0.5555556     0.5957763 -0.046408481 81   0.12 :(
##     time   error.diff shapes
##  1:  1.5 -0.077995802     16
##  2:  4.5 -0.051214048     24
##  3:  7.5 -0.021685483     16
##  4: 10.5 -0.068227022     24
##  5: 13.5 -0.074552535     24
##  6: 16.5 -0.048653420     16
##  7: 19.5 -0.029460170     16
##  8: 22.5 -0.035337710     16
##  9: 25.5 -0.003735285     16
## 10: 28.5 -0.046408481     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.68106  -0.15992  -0.08839   0.24816   0.75224  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.07430    0.02812   2.642  0.00847 ** 
## timeNorm     0.03609    0.03932   0.918  0.35921    
## obj.diff    -0.38936    0.02997 -12.991  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06627138)
## 
##     Null deviance: 47.552  on 550  degrees of freedom
## Residual deviance: 36.317  on 548  degrees of freedom
## AIC: 73.25
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.3736842     0.5824937 -0.22105237 38 0.00032 ***
##  2:      4.5      0.4052632     0.5178656 -0.09997599 57   0.0035 **
##  3:      7.5      0.4052632     0.4897451 -0.07737910 57     0.037 *
##  4:     10.5      0.4473684     0.4975966 -0.05160426 57     0.089 .
##  5:     13.5      0.3684211     0.4554127 -0.07786792 57     0.016 *
##  6:     16.5      0.3385965     0.4926908 -0.16375666 57 9.9e-05 ***
##  7:     19.5      0.3964912     0.4896047 -0.07782690 57    0.006 **
##  8:     22.5      0.4052632     0.5206631 -0.10748829 57   0.0074 **
##  9:     25.5      0.4877193     0.5600315 -0.04713849 57      0.1 :(
## 10:     28.5      0.4052632     0.5082965 -0.10381377 57   0.0058 **
##     time  error.diff shapes
##  1:  1.5 -0.22105237     24
##  2:  4.5 -0.09997599     24
##  3:  7.5 -0.07737910     24
##  4: 10.5 -0.05160426     16
##  5: 13.5 -0.07786792     24
##  6: 16.5 -0.16375666     24
##  7: 19.5 -0.07782690     24
##  8: 22.5 -0.10748829     24
##  9: 25.5 -0.04713849     16
## 10: 28.5 -0.10381377     24

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.71273  -0.14992  -0.08786   0.27418   0.49321  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.42032    0.06182   6.799 4.66e-11 ***
## timeNorm     0.10387    0.05362   1.937   0.0535 .  
## obj.diff    -0.79834    0.06101 -13.085  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07238212)
## 
##     Null deviance: 39.631  on 347  degrees of freedom
## Residual deviance: 24.972  on 345  degrees of freedom
## AIC: 78.791
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n        pval
##  1:      1.5      0.5375000     0.8667296 -0.336223188 24 8.3e-07 ***
##  2:      4.5      0.5666667     0.8503630 -0.300166002 36 8.1e-06 ***
##  3:      7.5      0.6500000     0.8149909 -0.191351300 36   0.0067 **
##  4:     10.5      0.6500000     0.7230354 -0.075959102 36     0.21 :(
##  5:     13.5      0.6277778     0.7492306 -0.168314672 36     0.043 *
##  6:     16.5      0.6944444     0.7843872 -0.103751069 36      0.1 :(
##  7:     19.5      0.6694444     0.7466016 -0.071855787 36     0.088 .
##  8:     22.5      0.5527778     0.7010553 -0.155156353 36     0.017 *
##  9:     25.5      0.6305556     0.6675804 -0.011808727 36     0.88 :(
## 10:     28.5      0.6638889     0.6510792  0.008180311 36     0.87 :(
##     time   error.diff shapes
##  1:  1.5 -0.336223188     24
##  2:  4.5 -0.300166002     24
##  3:  7.5 -0.191351300     24
##  4: 10.5 -0.075959102     16
##  5: 13.5 -0.168314672     24
##  6: 16.5 -0.103751069     16
##  7: 19.5 -0.071855787     16
##  8: 22.5 -0.155156353     24
##  9: 25.5 -0.011808727     16
## 10: 28.5  0.008180311     16
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.64791  -0.12252  -0.01687   0.08254   0.56550  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.412353   0.037259  11.067   <2e-16 ***
## timeNorm    -0.004306   0.043597  -0.099    0.921    
## obj.diff    -0.758214   0.039233 -19.326   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0555671)
## 
##     Null deviance: 44.748  on 405  degrees of freedom
## Residual deviance: 22.394  on 403  degrees of freedom
## AIC: -16.24
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n        pval
##  1:      1.5      0.4571429     0.5900756 -0.128331978 28     0.074 .
##  2:      4.5      0.5785714     0.7531825 -0.175486568 42 0.00038 ***
##  3:      7.5      0.5571429     0.5622264 -0.024952321 42     0.62 :(
##  4:     10.5      0.6166667     0.5363606  0.074535928 42     0.26 :(
##  5:     13.5      0.5857143     0.5877105 -0.004272840 42     0.89 :(
##  6:     16.5      0.5761905     0.5680560 -0.005940806 42     0.91 :(
##  7:     19.5      0.4833333     0.5083317 -0.026640700 42     0.56 :(
##  8:     22.5      0.5333333     0.4752222  0.063331692 42      0.3 :(
##  9:     25.5      0.5238095     0.4415357  0.079086798 42     0.13 :(
## 10:     28.5      0.4595238     0.3652122  0.102052175 42     0.089 .
##     time   error.diff shapes
##  1:  1.5 -0.128331978     16
##  2:  4.5 -0.175486568     24
##  3:  7.5 -0.024952321     16
##  4: 10.5  0.074535928     16
##  5: 13.5 -0.004272840     16
##  6: 16.5 -0.005940806     16
##  7: 19.5 -0.026640700     16
##  8: 22.5  0.063331692     16
##  9: 25.5  0.079086798     16
## 10: 28.5  0.102052175     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.65768  -0.19763  -0.04035   0.21009   0.72371  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.12696    0.02761   4.598    5e-06 ***
## timeNorm     0.06463    0.03613   1.789    0.074 .  
## obj.diff    -0.37517    0.03533 -10.619   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06922719)
## 
##     Null deviance: 61.766  on 753  degrees of freedom
## Residual deviance: 51.990  on 751  degrees of freedom
## AIC: 131.3
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n    pval
##  1:      1.5      0.3769231     0.4760639 -0.102437221 52 0.042 *
##  2:      4.5      0.4448718     0.4614922 -0.027765478 78 0.44 :(
##  3:      7.5      0.4205128     0.4300504 -0.017443021 78 0.62 :(
##  4:     10.5      0.4128205     0.4234581 -0.008671569 78 0.81 :(
##  5:     13.5      0.4294872     0.4001833  0.034151496 78 0.44 :(
##  6:     16.5      0.3897436     0.3337317  0.052219011 78 0.13 :(
##  7:     19.5      0.3461538     0.2730373  0.067485507 78 0.073 .
##  8:     22.5      0.3000000     0.2602781  0.014460765 78 0.59 :(
##  9:     25.5      0.3423077     0.2469083  0.092629756 78 0.011 *
## 10:     28.5      0.3333333     0.2303798  0.075968753 78 0.048 *
##     time   error.diff shapes
##  1:  1.5 -0.102437221     24
##  2:  4.5 -0.027765478     16
##  3:  7.5 -0.017443021     16
##  4: 10.5 -0.008671569     16
##  5: 13.5  0.034151496     16
##  6: 16.5  0.052219011     16
##  7: 19.5  0.067485507     16
##  8: 22.5  0.014460765     16
##  9: 25.5  0.092629756     24
## 10: 28.5  0.075968753     24

{r plot.subjective.objective.difficulty.confidence.scale, echo=FALSE} # #-------------------------------------------------------------------------------------- # # SHOWING SUBJECTIVE VS OBJECTIVE DIFFICULTY (CONFIDENCE SCALE APPROACH) # #-------------------------------------------------------------------------------------- # # plot.subjective.difficulty <- function(DT,selGroup,title){ # # print(selGroup) # # # Lien entre mise normalisée et difficultée estimée (hard / easy effect) # obj.diff.quants = seq(0,1,1/16)#quantile(DT$obj.diff, probs=(seq(0,1,0.05))) # nb.bins = length(obj.diff.quants)-1 # subj.diff.med = numeric(nb.bins) # obj.diff.bin = numeric(nb.bins) # obj.diff.bin.cur = 0; # test.pvals = numeric(nb.bins) # conf.min = numeric(nb.bins) # conf.max = numeric(nb.bins) # nb.vals = numeric(nb.bins) # shapes = numeric(nb.bins) # delta.obj.subj = numeric(nb.bins) # hist(DT$obj.diff) # for(i in 1:nb.bins){ # #obj.diff.bin.cur = round(i/10,1) # #subj.diff = DT[round(obj.diff,1)==obj.diff.bin.cur]$subj.diff.mise # obj.diff.bin.cur = (obj.diff.quants[i] + obj.diff.quants[i+1])/2.0 # #subj.diff = DT[obj.diff > obj.diff.quants[i] & obj.diff<=obj.diff.quants[i+1]]$subj.diff.mise # DTLoc = DT[obj.diff > obj.diff.quants[i] & obj.diff<=obj.diff.quants[i+1]] # if(selGroup != "all") # DTLoc = DTLoc[niveau.group==selGroup] # DTLoc = DTLoc[,.(confiance.mean=mean(subj.diff.confiance)),by=IDjoueur] # subj.diff = DTLoc$confiance.mean # obj.diff.bin[i] = obj.diff.bin.cur # subj.diff.med[i] = NA # test.pvals[i] = NA # conf.min[i] = NA # conf.max[i] = NA # delta.obj.subj[i] = NA # shapes[i] = 16 # nb.vals[i] = length(subj.diff) # if(nb.vals[i] > 1){ # try.res = try(test.res <- wilcox.test(subj.diff,mu = obj.diff.bin.cur,conf.int=T)) # if (class(try.res) != "try-error"){ # #print(test.res) # #hist(subj.diff) # test.pvals[i] = format.pval.stars(test.res$p.value) # if(test.res$p.value < 0.05) # shapes[i] = 24 # #subj.diff.med[i] = mean(subj.diff) # subj.diff.med[i] = test.res$estimate # conf.min[i] = test.res$conf.int[1] # conf.max[i] = test.res$conf.int[2] # delta.obj.subj[i] = signif(subj.diff.med[i] - obj.diff.bin.cur,digit=2) # } # } # } # # #print table of pvalues # print(data.table(obj.diff.bin=obj.diff.bin,delta.obj.subj=delta.obj.subj,n=nb.vals,pval=test.pvals)) # # #summary # print("mean and sd of nb players per bin") # DTNbVals = data.table(nb = nb.vals, pval=test.pvals) # print(DTNbVals[!is.na(pval)]) # print(signif(mean(DTNbVals[!is.na(pval)]$nb),digits=3)) # print(signif(sd(DTNbVals[!is.na(pval)]$nb),digits=3)) # # #kernel smooth # subj.diff.smooth <- ksmooth(x=DT$obj.diff,y=DT$subj.diff.confiance,bandwidth = 0.2) # DTSmooth = data.table(x=subj.diff.smooth$x,y=subj.diff.smooth$y) # # DTPlot = data.table(obj.diff=obj.diff.bin,subj.diff=subj.diff.med, shapes=shapes) # # p = ggplot() + ggtitle(title) + # # geom_line(aes(x=DTPouet$x,y=DTPouet$y))+ # geom_point(aes(x=DTPlot$obj.diff,y=DTPlot$subj.diff),alpha = 1, size = 3, shape=DTPlot$shapes) + # xlim(0,1)+ # ylim(0,1)+ # geom_errorbar(aes(x=DTPlot$obj.diff, ymin=conf.min, ymax=conf.max), width=.01,color="red") + # geom_abline(intercept = 0, slope = 1, color="blue") + # xlab("Objective Difficulty") + ylab("Subjective Difficulty") + theme(text = element_text(size=15)) # # print(p) # } #

All tasks

{r plot.subjective.difficulty.all.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTAll,"all", "All tasks, all groups") # plot.subjective.difficulty(DTAll,"good", "All tasks, good") # plot.subjective.difficulty(DTAll,"medium", "All tasks, medium") # plot.subjective.difficulty(DTAll,"bad", "All tasks, bad") #

Motor task

{r plot.subjective.difficulty.motor.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTM,"all", "Motor, all") # plot.subjective.difficulty(DTM,"good", "Motor, good") # plot.subjective.difficulty(DTM,"medium", "Motor, medium") # plot.subjective.difficulty(DTM,"bad", "Motor, bad") #

Sensory task

{r plot.subjective.difficulty.sensory.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTS,"all","Sensory, all") # plot.subjective.difficulty(DTS,"good","Sensory, good") # plot.subjective.difficulty(DTS,"medium","Sensory, medium") # plot.subjective.difficulty(DTS,"bad","Sensory, bad") #

Logical task

{r plot.subjective.difficulty.logical.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTL,"all","Logical, all") # plot.subjective.difficulty(DTL,"good","Logical, good") # plot.subjective.difficulty(DTL,"medium","Logical, medium") # plot.subjective.difficulty(DTL,"bad","Logical, bad") #